I have a lot of fun teaching first year biology undergraduates but there are a few challenges in teaching data skills when they are not (perceived as) a student’s core discipline but instead required to carry out research within it. At this early stage in their higher education, Biologists can be surprised by the amount of their degree devoted to data analysis, reporting and presentation.

In my introductory lecture I use polling software to get responses from my students to:

As you can see, my students don’t mind making their feelings clear!

Those are the results from the last two years – if anything, this year’s students are more sure they won’t enjoy it! I suspect this is not the result my colleagues teaching Genetics, Evolution, Cell Biology or Development would get if they asked the same. And understandably, I think.

This year I set Essential Prior Independent Study using the ability to set “Assignments” for a team in my DataCamp‘ classroom. I had them do only the first two chapters (Intro to basics and Vectors) of Introduction to R. Last year I suggested DataCamp as an optional activity and used part of it in an introductory workshop.

I was delighted to see that well over half the class of 256 students had started or completed the assignments before the lecture despite the assignment deadline still being a day away. And there was more……..when I asked how they felt about R, they were more positive than last year:

How good is that? Many ‘Seems Ok’ and ‘Undecided’ and more students excited than terrified is a win!

Help! The same annotations go on every facet!

This is a question I get fairly often and the answer is not straightforward especially for those that are relatively new to R and ggplot2.

In this post, I will show you how to add different annotations to each facet. More like this:

This is useful in its own right but can also help you understand ggplot better.

I will assume you have R Studio installed and have at least a little experience with it but I’m aiming to make this do-able for novices. I’ll also assume you’ve analysed your data so you know what annotations you want to add.

Faceting is a very useful feature of ggplot which allows you to efficiently plot subsets of your data next to each other.
In this example the data are the wing lengths for males and females of two imaginary species of butterfly in two regions, north and south. Some of the results of a statistical analysis are shown with annotation.

1. Preparation

The first thing you want to do is make a folder on your computer where your code and the data for plotting will live. This is your working directory.
Now get a copy of the data by saving this file the folder you just made.

2. Start in RStudio

Start R Studio and set your working directory to the folder you created:

Now start a new script file:

and save it as figure.R or similar.

3. Load packages

Make the packages you need available for use in this R Studio session by adding this code to your script and running it.

# package loading
library(ggplot2)
library(Rmisc)

4. Read the data in to R

The data are in a plain text file where the first row gives the column names. It can be read in to a dataframe with the read.table() command:

butter <- read.table("butterflies.txt", header = TRUE)

This each row in this data set is an individual butterfly and the columns are four variables:

winglen the wing length (in millimeters) of an individual

spp its species, one of “F.concocti” or “F.flappa”

sex its sex, one of “female” or “male”

region where it is from, one of “North” or “South”

5. Summarise the data

Our plot has the means and standard errors for each group and this requires us to summarize over the replicates which we can do with the summarySE() function:

aes(x = spp, y = winglen, colour = sex) the “aesthetic mappings” specify where to put each variable. Aesthetic mappings given in the ggplot() statement will apply to every “layer” in the plot unless otherwise specified.

geom_point() the first “layer” adds points

position = position_dodge(width = 1) indicates female and male means should be plotted side-by-side for each species not on top on each other

geom_errorbar() the second layer adds the error bars. These must also be position dodged so they appear on the points.

aes(ymin = winglen - se, ymax = winglen + se) The error bars need new aesthetic mappings because they are not at winglen (the mean in the summary) but at the mean – the standard error and the mean + the standard error. Since all of that information is inside butterNsum, we do not need to give the data argument again.

d) Annotate this plot (i)

The annotation is composed of three lines – or segments – and some text. Each segment has a start (x, y) and an end (xend, yend) which we need to specify. The text is centered on its (x, y)

The x-axis has two categories which have the internal coding of 1 and 2. We want the annotation to start a bit before 2 and finish a bit after 2.

Note that position_dodge() units are twice the category axis units in this example.

Then give a dataframe argument to geom_segment() and geom_text() and the aesthetic mappings for that dataframe. We also need to move the colour mapping from the ggplot() statement to the geom_point() and geom_errorbar().

This is because the mappings applied in the ggplot() will apply to every layer unless otherwise specified and if the colour mapping stays there, geom_segment() and geom_text() will try to find the variable ‘sex’ in the anno dataframe.

It’s easy to animate graphs in R

This is something you might want to do to increase the impact of your work by communicating it through twitter, a website or live in a presentation.
Imagine you have carried out an experiment to test whether the growth of different Ralstonia solanacearum strains could be inhibited by a treatment X. R.solanacearum is a bacterium which causes many plant diseases so controlling it is beneficial. You measured the growth of each strain of R.solanacearum with and without the potential inhibitor (treatment X vs control) each day for 20 days and you did five replicates of each treatment combination.

Your dataset will have: 5 strains x 2 treatments x 20 days x 5 replicates = 1000 rows. For a report or article you might highlight the difference between the strains in their response to the treatment by plotting the last time point only. Something like this:

Or perhaps you’d like to emphasize the effects of treatment and time (and average over the strains) like this:

Or try to show time, strain and treatment on one graph:

However, if your communication medium allows it, animation is a space-efficient and easily understood way to illustrate your results.

I will assume you have R Studio installed and have at least a little experience with it but I’m aiming to make this do-able for novices.

1. Preparing

The first thing you want to do is make a folder on your computer in where your code and the data for plotting will live. This is your working directory.
Now get a copy of the data by saving this file. The important thing is to save it to your working directory (the folder you just made).

2. Getting started in RStudio

Start R Studio and set your working directory to the folder you created:

Now start a new script file:

and save it as animatedfigure.R or similar.

You will need the devtools package to install gganimate

devtools and gganimate are already installed for biologists at the University of York. If you are on your own computer you will need to install them.

To do this you first install devtools in the normal way. Click the install button and write the package name in the box to install it.

R Studio may need to install additional packages. You will also need the Rmisc package for summarizing data so install that too.

You can now use the install_github() function in the devtools package to install gganimate:

Share this:

Like this:

It’s a challenge for an experienced user to remember what it was like to be totally new to R and come up with explanations that don’t draw on understanding developed subsequently. Terminology with which you have become very familiar is, in fact, jargon. So I asked a novice, Elliot, to explain a piece of code in his own words. Elliot is a 17 year old student half way through a BTEC Level 3 Extended Diploma in Science which is a qualification which provides access to Higher Education in the UK.

I have annotated with the jargon. In doing so I realised that the c() function is perhaps not the best example but that’s for another post.R code Anatomy – a nice big image

Share this:

Like this:

RStudio makes R easier to use. It includes a code editor, debugging & visualization tools. I love it but when beginners launch RStudio they are sometimes confused by all the panes and tabs. Here I have tried to give a quick visual guide to the anatomy of RStudio for people new to R, coding and RStudio. I’d love to know if I’ve missed anything or I’ve if unintentionally used jargon!RStudio Anatomy – a nice big image

Share this:

Like this:

Remember to reference R

When people are new to using R and, perhaps, to referencing and report writing in general, they often don’t know they should cite and reference R and its packages. We do this for the same reasons we reference any thing else in any academic work.

We need to support our arguments with evidence and give readers the opportunity to evaluate the validity of that evidence. Citing R and its packages allows people to evaluate the reproducilibity of your analysis and results.

We need to recognise and give credit for the work of others. R is a collaborative open source project with many contributors and citing R and its packages supports the development of such fantastic and free tools.

R version 3.4.2 (2017-09-28) -- "Short Summer"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

Share this:

Like this:

Code for fun

I feel passionately that anyone can learn some coding but many feel they lack natural aptitude for it.
One of the things I always try to stress to those I am teaching is that it matters very much less what you do than that do you it. It doesn’t matter how small and trivial the result of the code is, or even how much of it you wrote yourself. Any work you do with code at all, will build your confidence, experience and skills.

Now I’m going to show you how to plot some data using one image as a marker and a different one for the back ground!

I will assume you have Rstudio installed and have at least a little experience with it but I’m aiming to make this do-able for beginners. I’ll also be using ggplot2 rather than base R.

1. Preparing

The first thing you want to do is make a folder on your computer in which the code you write and your images will live. Now you need some images.
I’m going to this as my background

and this as my marker

You can use the same ones or use choose your own favourite images – try googling “public domain images”. You don’t want very high resolution images for the sake of speed but otherwise the size doesn’t matter.

The important thing is to save the images in to the folder you just created.

2. Getting started in RStudio

Start RStudio and set your working directory to the folder you created:

Now start a new script file:

and save it as funplot.R or similar.

There are several packages you will need. If your images files are png like mine: ggplot2, png, grid, ggimage.
If your images files are jpeg: ggplot2, jpeg, grid, ggimage.

All these are already installed for biologists at the University of York. If you are on your own computer you will need to install them.

Click the install button and write the package names in the box to install them. I recommending doing them one at a time.

Rstudio may need to install additional packages.

When everything is finished, you are ready to start coding!

3. Starting to code

Make the packages available for use in this Rstudio session by adding this code to your script and running it.

library(ggplot2)
library(png)
library(grid)
library(ggimage)

4. Making some data to plot

I am going to plot the number of bees arriving within one hour at food sources placed at different distances from the hive. The data are as follows:

Make sure you put the annotation_custom() before the geom_point() but don’t worry too much about what the other bits mean.

To use bees as markers we need to add the bee image file name to every row of the data frame:

bees$image <- "bee.png"

This adds a column called image to the dataframe called bees. We don’t have to explicitly read the image in because the ggimage package we loaded takes care of that. ggimage is an ‘extension’ to ggplot2.

Now edit your graph code again by removing the geom_point() and adding a geom_image() instead like this:

Share this:

Like this:

… and non-normally distributed data can be normal

One of the underlying assumptions of many statistical methods is that the data (or the model residuals) are normally distributed. I teach students to evaluate this assumption with plots and normality tests. When they find their data do not seem to be normally distributed, they often report:

“… the data is abnormal”

This is incorrect, and not just because of the grammar. It arises when the name of the distribution confused is with the everyday-use of the word “normal”.

It’s true that the Normal distribution has acquired its name because seeing it is quite normal; many variables are normally distributed. Similarly, the Common gull (Lanus canus) is so-called because it is commonly seen. However, not all commonly seen gulls are Common gulls. Great black-backed gulls (Larus marinus) are not uncommon gulls.

There are several distributions which are common, usual and normal to see! For example, it is normal for counts to follow a Poisson distribution. Poisson data are definitely not Normal but they are not abnormal.

If your data are not normally distributed you might report:

“… the data are not normally distributed”

to be statistically and grammatically correct.

If it is not a Common gull it could be the common great black-backed gull or a herring gull.

Note: Yes, I do see ‘the data is’ much more frequently than the grammatically correct ‘the data are’